In Degrees All

SQL function: cugraph_in_degrees_all

Official cuGraph reference: Python API

Count incoming edges for every vertex in the graph.

Signature

cugraph_in_degrees_all(table_name [, src_col, dst_col [, weight_col [, options_json]]])

Relation inputs

The first positional argument names a registered edge table or view (the edges role). Parenthesized relation subqueries are not accepted; metadata validation uses the same registered name.

Vertex ID types

The edges relation declares the accepted vertex-ID domains. Numeric calls preserve the existing numeric schema. When logical string support is declared, Utf8, LargeUtf8, and Utf8View endpoint columns share one logical domain; their vertex-identity outputs are canonicalized to Utf8.

Domain	Accepted endpoint inputs	Output contract
Numeric edge endpoints	Int32, Int64	The numeric output schema is used for numeric calls.
Logical string edge endpoints	Utf8, LargeUtf8, Utf8View	Vertex identity columns are canonicalized to Utf8; scores, distances, counts, coordinates, and opaque labels remain numeric.

The native mapping type is Int64. Call-specific output schemas come from gpu_validate_call.

Logical string side-input limitations:

• edge ID columns and edge-ID predicate side inputs are not supported for logical string graphs

Scalar arguments & JSON options

Positional scalar arguments

src_col and dst_col name the edge endpoint columns; both are optional and default to src and dst.

Argument	Type	Required	Default	Notes
weight_col	Utf8|null	no		accepted as an edge-column binding; native algorithm execution does not consume weights; semantic effect: none for this algorithm

JSON options

This function has no algorithm-specific options.

Graph construction options

Graph construction follows the shared defaults (directed=true, renumbering, python_cugraph policy) documented in Graph Construction Options.

Output schema

Column	Type	Nullable	Description
vertex	Int64|Utf8	no	Vertex whose in-degree count is reported.
in_degree	Int64	no	Number of incoming edges for the vertex.

These are generic descriptor schemas; validate the call to get the concrete, table-specific output schema.

Examples

This example runs on the citation network demo dataset.

In-graph citations versus reported metadata

papers.n_citation is AMiner's global citation count; in_degree counts only edges present in this corpus. Joining the two measures how much of each paper's audience lies outside the dataset:

SELECT p.year, d.in_degree AS in_graph, p.n_citation AS reported,
       p.n_citation - d.in_degree AS outside,
       p.title AS paper
FROM cugraph_in_degrees_all('citation_edges', 'src', 'dst') d
JOIN papers p ON p.paper_id = d.vertex
ORDER BY d.in_degree DESC
LIMIT 5;

year	in_graph	reported	outside	paper
2004	22,892	35,541	12,649	Distinctive Image Features from Scale-Invariant Keypoints
2011	19,662	31,047	11,385	LIBSVM: A library for support vector machines
1989	17,207	44,175	26,968	Genetic algorithms in search, optimization, and machine learning
1996	16,579	42,437	25,858	Fuzzy sets
2001	15,048	34,741	19,693	Random Forests

SIFT is the most-cited paper within the corpus. Older cross-disciplinary classics (genetic algorithms, fuzzy sets) show the biggest gap — more than half their citations come from fields this CS-centric corpus doesn't cover.

Limitations & lifecycle

No algorithm-specific limitations.

Validate before running

Dry-run validation checks registered relation metadata, column presence, static dtypes, and options only; it does not scan edge data, construct a graph, or prove source-vertex existence:

SELECT * FROM gpu_validate_call(
  'cugraph_in_degrees_all',
  '{"schema_version":1,"relations":{"edges":{"table":"target_edges"}},"options":{"src_col":"src","dst_col":"dst"}}'
);

See GPU Function Catalog API for the full gpu_validate_call contract.